Selective Plane Illumination Microscopy (SPIM) is a fluorescence imaging technique that allows volumetric imaging at high spatio-temporal resolution to monitor neural activity in live organisms such as larval zebrafish. A major challenge in the construction of a custom SPIM microscope using a scanned laser beam is the control and synchronization of the various hardware components.
We present an open-source software, μSPIM Toolset, built around the widely adopted MicroManager platform, that provides control and acquisition functionality for a SPIM. A key advantage of μSPIM Toolset is a series of calibration procedures that optimize acquisition for a given set-up, making it relatively independent of the optical design of the microscope or the hardware used to build it.
μSPIM Toolset allows imaging of calcium activity throughout the brain of larval zebrafish at rates of 100 planes per second with single cell resolution.
Several designs of SPIM have been published but are focused on imaging of developmental processes using a slower setup with a moving stage and therefore have limited use for functional imaging. In comparison, μSPIM Toolset uses a scanned beam to allow imaging at higher acquisition frequencies while minimizing disturbance of the sample.
The μSPIM Toolset provides a flexible solution for the control of SPIM microscopes and demonstrated its utility for brain-wide imaging of neural activity in larval zebrafish.
The μSPIM Toolset provides a flexible solution for the control of SPIM microscopes and demonstrated its utility for brain-wide imaging of neural activity in larval zebrafish.Analytical methods of brain research involving across-voxel correlation between multimodal images are currently tedious and slow due to the amount of manual interaction required. We have developed a new software package to automate and simplify many of these tasks.
Our software performs four primary functions to aid in research. First, it helps with consistent renaming of files preprocessed with other software, enabling more accurate analysis. Second, it automates ROI extraction using data from existing and custom brain atlases. Third, it performs coupling analysis to obtain across-voxel Pearson correlation coefficients between images of different modalities based on these brain atlases or custom ROIs. Fourth, it automatically performs multiple comparison correction to correct the P-value using two false discovery rate (FDR) methods and a Bonferroni method to reduce the false-positive rate.
Previous researchers have investigated the couplings between blood supply and brain functional topology in healthy brains and those from patients with type 2 diabetes, chronic migraine, and schizophrenia. These studies conducted analyses of both the whole and parts of the brain in terms of neuronal activity and blood perfusion, but the procedures were laborious and time-consuming.
We have developed a convenient and time-saving software package using MATLAB 2014a to automate the data preparation and analysis of across-voxel coupling between multimodal images.
We have developed a convenient and time-saving software package using MATLAB 2014a to automate the data preparation and analysis of across-voxel coupling between multimodal images.Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. https://www.selleckchem.com/products/U0126.html We conclude the review with some future directions and recommendations on this topic.Parkinson's disease (PD) is becoming a major public-health issue in an aging population. Available approaches to treat advanced PD still have limitations; new therapies are needed. The non-invasive brain stimulation (NIBS) may offer a complementary approach to treat advanced PD by personalized stimulation. Although NIBS is not as effective as the gold-standard levodopa, recent randomized controlled trials show promising outcomes in the treatment of PD symptoms. Nevertheless, only a few NIBS-stimulation paradigms have shown to improve PD's symptoms. Current clinical recommendations based on the level of evidence are reported in Table 1 through Table 3. Furthermore, novel technological advances hold promise and may soon enable the non-invasive stimulation of deeper brain structures for longer periods.Older adults are impaired at implicit associative learning (IAL), or the learning of relationships between stimuli in the environment without conscious awareness. These age effects have been attributed to differential engagement of the basal ganglia (e.g. caudate, globus pallidus) and hippocampus throughout learning. However, no studies have examined gray matter diffusion relations with IAL, which can reveal microstructural properties that vary with age and contribute to learning. In this study, young (18-29 years) and older (65-87 years) adults completed the Triplet Learning Task, in which participants implicitly learn that the location of cues predict the target location on some trials (high frequency triplets). Diffusion imaging was also acquired and multicompartment diffusion metrics were calculated using neurite orientation dispersion and density imaging (NODDI). As expected, results revealed age deficits in IAL (smaller differences in performance to high versus low frequency triplets in the late learning stage) and age-related differences in basal ganglia and hippocampus free, hindered, and restricted diffusion.